Linear regression and classification methods with repeated functional data are considered. For each statistical unit in the sample, a real-valued parameter is observed over time under different conditions. Two regression methods based on fusion penalties are presented. The first one is a generalization of the variable fusion methodology based on the 1-nearest neighbor. The second one, called group fusion lasso, assumes some grouping structure of conditions and allows for homogeneity among the regression coefficient functions within groups. A finite sample numerical simulation and an application on EEG data are presented.

翻译：本文探讨了针对重复函数数据的线性回归与分类方法。对于样本中的每个统计单元，在不同条件下随时间观测到一个实值参数。提出了两种基于融合惩罚的回归方法：第一种是基于1-近邻的变量融合方法的推广；第二种称为组融合套索（group fusion lasso），该方法假设条件存在某种分组结构，并允许组内回归系数函数具有同质性。通过有限样本数值仿真和脑电图（EEG）数据应用进行了验证。